- A
Increase the number of replicas to reduce error rate.
Why wrong: May temporarily mask the problem if errors are due to overload, but does not fix root cause.
- B
Compare the input data distribution of recent requests to the training data distribution using Explainable AI.
Why wrong: Useful but not the first step; requires setup and may not reveal immediate causes.
- C
Roll back to the previous model version immediately.
Why wrong: Rolling back without investigation may not fix the issue and could disrupt service.
- D
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
Monitoring and logs provide direct evidence to diagnose errors.
Question 877 of 1,000
mediumMultiple ChoiceObjective-mapped
Diagnose Increased Prediction Errors After Redeployment
This PMLE practice question tests your understanding of pmle exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning engineer notices that the Vertex AI Prediction endpoint's error rate has increased over the past week. The model was retrained with new data and redeployed. Which step should the engineer take first to diagnose the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Quick Answer
The answer is to check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs. This is the correct first step because diagnosing prediction errors after model redeployment requires immediate visibility into system-level metrics and request-level data; latency spikes or specific HTTP error codes (like 503 or 500) in Cloud Monitoring can reveal infrastructure issues, while prediction logs expose data drift or feature mismatches between training and serving. On the Google Professional Machine Learning Engineer exam, this tests your understanding of the ML lifecycle’s monitoring phase—specifically that root-cause analysis must begin with observability tools before jumping to retraining or scaling. A common trap is assuming the new data is flawed (Option A) or immediately enabling model monitoring (Option B), which requires prior configuration. Memory tip: “Monitor first, diagnose second—logs and metrics are your detective’s lens.”
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
The correct first step is D because checking Cloud Monitoring dashboards and reviewing prediction logs provides immediate visibility into error patterns and root causes, such as data shifts or infrastructure issues. Option A (increasing replicas) addresses symptoms but not the cause. Option B (Explainable AI) is more advanced and typically used after initial diagnostics. Option C (rolling back) may restore previous performance but does not identify why the new model failed, and could discard improvements.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of replicas to reduce error rate.
Why it's wrong here
May temporarily mask the problem if errors are due to overload, but does not fix root cause.
- ✗
Compare the input data distribution of recent requests to the training data distribution using Explainable AI.
Why it's wrong here
Useful but not the first step; requires setup and may not reveal immediate causes.
- ✗
Roll back to the previous model version immediately.
Why it's wrong here
Rolling back without investigation may not fix the issue and could disrupt service.
- ✓
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
Why this is correct
Monitoring and logs provide direct evidence to diagnose errors.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs. — The correct first step is D because checking Cloud Monitoring dashboards and reviewing prediction logs provides immediate visibility into error patterns and root causes, such as data shifts or infrastructure issues. Option A (increasing replicas) addresses symptoms but not the cause. Option B (Explainable AI) is more advanced and typically used after initial diagnostics. Option C (rolling back) may restore previous performance but does not identify why the new model failed, and could discard improvements.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 24, 2026
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